This invention is in the field of the drilling of wells, and is more specifically directed to measurement and control systems for use in such drilling.
As is fundamental in the art, the drilling of wells consumes a large portion of the cost involved in the exploration for and production of oil and gas. Drilling costs have increased substantially in recent years, considering that many of the easily discovered and accessible fields in the world are already producing, if not already tapped out. As such, new wells to reach such less-accessible reservoirs are generally much deeper, and otherwise much more complex, than in years past. New wells are also often drilled at locations of reduced confidence that a producing potential reservoir is present, because of the extreme depth of the remaining reservoirs. Even when drilling into more certain hydrocarbon reservoirs, drilling costs are also often higher than in the past because of the inaccessibility of the reservoirs (e.g., at locations far offshore), or other local difficulties.
Because of these increasing costs involved in modern drilling, it is ever more critical that the drilling operation be carried out accurately and efficiently. The criticality of accurate drilling is also especially important as smaller potential reservoirs, at greater depths into the earth, are being exploited. In addition, the extreme depths to which modern wells are now being drilled add many complications to the drilling process, including the cost and effort required to address drilling problems that may occur at such extreme depths and with such increased well complexity. A very high level of skill is thus required of the driller or drilling engineer, who is the primary decision-maker at the drilling rig, in order to safely drill the well as planned. But these skills are in short supply.
On the other hand, as known in the art, a tremendous amount of information and computer processing power is available from modern computing equipment and techniques. The technology available for sensors, and for communicating and processing signals from sensors, continues to advance; in addition, modern techniques for data acquisition have also greatly improved, due in large part to the massive computing power now locally available at relatively modest cost.
By way of further background, the failure mechanism of “lost circulation” is a known concern in the drilling of an oil or gas well. As is fundamental in the art, drilling “mud” is circulated through the drill string during drilling to lubricate and perhaps power the drill bit itself, and to return cuttings to the surface; the drilling mud is cleaned to remove the cuttings and other material, and is then recycled into the drill string. Lost circulation refers to the situation in which the drilling mud is lost into the formation, rather than returning to the surface. Besides the obvious economic cost of replacing the relatively expensive drilling mud, lost circulation can also cause more catastrophic failures such as stuck drill pipe, blowout of the well, damage to the reservoir itself, and loss of the well altogether.
By way of further background, the term “software agent” is known in the art as referring to a computer software program or object that is capable of acting in a somewhat autonomous manner to carry out one or more tasks on behalf of another program or object in the system. Software agents can also have one or more other attributes, including mobility among computers in a network, the ability to cooperate and collaborate with other agents in the system, adaptability, and also specificity of function (e.g., interface agents). Some software agents are sufficiently autonomous as to be able to instantiate themselves when appropriate, and also to terminate themselves upon completion of their task.
By way of further background, the term “expert system” is known in the art as referring to a software system that is designed to emulate a human expert, typically in solving a particular problem or accomplishing a particular task. Conventional expert systems commonly operate by creating a “knowledge base” that formalizes some of the information known by human experts in the applicable field, and by codifying some type of formalism by way the information in the knowledge base applicable to a particular situation can be gathered and actions determined. Some conventional expert systems are also capable of adaptation, or “learning”, from one situation to the next. Expert systems are commonly considered to in the realm of “artificial intelligence”.
By way of further background, the term “knowledge base” is known in the art to refer to a specialized database for the computerized collection, organization, and retrieval of knowledge, for example in connection with an expert system.
By way of further background, the term “rules engine” is known in the art to refer to a software component that executes one or more rules in a runtime environment providing among other functions, the ability to: register, define, classify, and manage all the rules, verify consistency of rules definitions, define the relationships among different rules, and relate some of these rules to other software components that are affected or need to enforce one or more of the rules. Conventional approaches to the “reasoning” applied by such a rules engine in performing these functions involve the use of inference rules, by way of which logical consequences can be inferred from a set of asserted facts or axioms. These inference rules are commonly specified by means of an ontology language, and often a description language. Many reasoners use first-order predicate logic to perform reasoning; inference commonly proceeds by forward chaining and backward chaining.
By way of further background, the use of automated computerized system to gather measurement data from an oil or gas well during drilling, and to display trend information for those measurements at the rig location, is known. One such conventional system gathers such measurement data including bottomhole pressure, temperature, flow, torque and turn information and the like. In that conventional system, a display is generated to indicate pressure differences (i.e., differences between bottomhole pressure and formation pressure) versus drilling depth.